With the deep integration between the unmanned aerial vehicle (UAV) and wireless communication, UAV-based air-to-ground (AG) propagation channels need more detailed descriptions and accurate models. In this paper, we aim to conduct cluster-based characterization and modeling for AG channels. To our best knowledge, this is the first study that concentrates on the clustering and tracking of multipath components (MPCs) for time-varying AG channels. Based on measurement data at 6.5 GHz with a bandwidth of 500 MHz, we first estimate potential MPCs utilizing the space-alternating generalized expectation-maximization (SAGE) algorithm. Then, we cluster the extracted MPCs by employing K-Power-Means (KPM) algorithm under multipath component distance (MCD) measure. For characterizing time-variant clusters, we exploit a clustering-based tracking (CBT) method, which efficiently quantifies the survival lengths of clusters. Ultimately, we establish a cluster-based channel model, and validations illustrate the accuracy of the proposed model. This work not only promotes a better understanding of AG propagation channels but also provides a general cluster-based AG channel model with certain extensibility.

中文翻译：

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无人机空对地时变信道的基于集群的表征和建模

随着无人机（UAV）与无线通信的深度融合，基于无人机的空对地（AG）传播通道需要更详细的描述和准确的模型。在本文中，我们旨在对 AG 通道进行基于集群的表征和建模。据我们所知，这是第一项专注于时变 AG 信道的多路径分量 (MPC) 的聚类和跟踪的研究。基于 6.5 GHz 和 500 MHz 带宽的测量数据，我们首先利用空间交替广义期望最大化 (SAGE) 算法估计潜在的 MPC。然后，我们在多路径分量距离 (MCD) 测量下采用 K-Power-Means (KPM) 算法对提取的 MPC 进行聚类。为了表征时变集群，我们利用基于聚类的跟踪（CBT）方法，它有效地量化了集群的生存长度。最终，我们建立了一个基于集群的通道模型，并且验证说明了所提出模型的准确性。这项工作不仅促进了对 AG 传播信道的更好理解，而且提供了一个通用的基于集群的 AG 信道模型，具有一定的可扩展性。